The present disclosure relates to a video-based method and system for detecting vehicle data based on size characteristics of a candidate region within an image capture device's field of view. The disclosure finds application in parking space management and enforcement. However, it is appreciated that the present exemplary embodiments are also amendable to other like applications.
A video-based parking management system monitors select parking areas to provide real-time vehicle detection and parking occupancy data. One area that is monitored by the system can include on-street parking lanes. These areas can be divided into stalls for transportation and parking space management purposes. FIG. 1A illustrates an on-street single-space parking scenario, also known as stall-based parking, in which each parking space is defined in a parking area by clear boundaries. An on-street parking area 10 extends along a curb on a street 12. The parking area 10 is more specifically defined by a parking lane that is divided into a number of single spaces 14-18, each designating one space per vehicle using markings (shown in phantom) on the street. Three vehicles 20-24 are parked in the parking area 10 and each vehicle is properly parked inside the boundaries of a space 14-18. Video cameras 26 are installed nearby for continuously monitoring the parking area 10. The cameras 26 provide video feed to a processor of the system, which analyzes the video data to determine whether there is an available space 14-18.
Mainly, this management system can inform a user about the availability of parking spaces for reducing fuel consumption and traffic congestion. In another scenario, video-based technology can monitor vehicle positions relative to parking space boundaries for enforcing parking boundary regulations as well as other law enforcement regulations.
This known system localizes a parked vehicle within a candidate region where a vehicle is known to potentially exist. The system detects the vehicle by exploiting spatial and temporal correlation between neighboring frames in a video sequence. While the system can detect passenger vehicles with high accuracy, it can partially detect and/or miss larger-sized vehicles, such as commercial vehicles including, but not limited to, certain trucks, semi-trailers, busses, coaches, trailers, and commercial vans.
FIGS. 1B and 1C show an example scenario where this error may occur as a result of the conventional method, which applies a single classifier that is trained by using a mixture (i.e., percentage) of passenger vehicles (i.e., cars) and (to) large vehicles (e.g., trucks or buses). The fraction of the mixture is a direct outcome of what was observed on-site. The fraction of large vehicles is typically very small for a street, and thus the accuracy of detecting large vehicles is typically lower when using this approach. Consider the following problematic example. A first vehicle 28 is properly parked within the boundaries of the space 14. However, a second larger vehicle 30 is parked within the boundaries of a space 16 and into at least one adjacent space 18, thus occupying multiple spaces 16, 18. The system divides the commercial vehicle 28 into multiple objects, each of which is treated as a separate passenger vehicle which is an inaccurate representation of the actual parked vehicles.
One contemplated method is to train the single classifier in the known system using a mixture of different types of vehicles across a range of various sizes. However, the variety of vehicles used to train the classifier can weaken accuracy since it increases the possibility of a misidentified object. More specifically, a decision is made for what fraction of the mixture should be included in the training for each site. This decision is challenging because it may cause a potential trade-off between vehicle types, by strengthening the identification of one vehicle type at the expense (i.e., by weakening the identification) of another. In certain scenarios, the identification of both vehicle types can weaken due to confusion. For example, if the training set contains mostly passenger vehicles, this single classifier may not perform well for classifying large vehicles as shown in FIG. 1C. The classification of larger vehicles may improve by increasing the fraction of large vehicles in the training set, but this approach may in-turn weaken the classification of passenger vehicles. To this extreme, if a significant number of large vehicles are included in the training set, the classifier may start to fail at classifying passenger vehicles. Even if a good mixture exists of the various types of vehicles for training a single classifier, the process, to meet a preferred mixture, can be manually intensive and cumbersome to implement for each camera in a large scale deployment.
Because the presence of the larger-sized vehicle can cause the known system to err when it estimates the availability and/or number of parking spaces, an improved video-based approach is desired which more accurately detects passenger and larger-sized vehicles